Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90259
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWei, Xiaolingen
dc.contributor.authorLi, Jiminen
dc.contributor.authorZhang, Chenghaoen
dc.contributor.authorLiu, Mingen
dc.contributor.authorXiong, Pengen
dc.contributor.authorYuan, Xinen
dc.contributor.authorLi, Yifeien
dc.contributor.authorLin, Fengen
dc.contributor.authorLiu, Xiulingen
dc.date.accessioned2019-05-29T07:58:08Zen
dc.date.accessioned2019-12-06T17:44:14Z-
dc.date.available2019-05-29T07:58:08Zen
dc.date.available2019-12-06T17:44:14Z-
dc.date.issued2019en
dc.identifier.citationWei, X., Li, J., Zhang, C., Liu, M., Xiong, P., Yuan, X., . . . Liu, X. (2019). Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network. Journal of Probability and Statistics, 2019, 8057820-. doi:10.1155/2019/8057820en
dc.identifier.issn1687-952Xen
dc.identifier.urihttps://hdl.handle.net/10356/90259-
dc.identifier.urihttp://hdl.handle.net/10220/48455en
dc.description.abstractIn this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.en
dc.format.extent10 p.en
dc.language.isoenen
dc.relation.ispartofseriesJournal of Probability and Statisticsen
dc.rights© 2019 Xiaoling Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.subjectAtrial Fibrillation Detectionen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.subjectNeural Networken
dc.titleAtrial fibrillation detection by the combination of recurrence complex network and convolution neural networken
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.identifier.doi10.1155/2019/8057820en
dc.description.versionPublished versionen
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:SCSE Journal Articles

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.